This invention relates to linear predictive coding. In particular, it is an improved method and means of determining coefficients for linear predictive coding. Linear predictive coding is a method of analyzing a speech signal and characterizing that signal in terms of coefficients which can be encoded, broadcast, received, and decoded to recover a close approximation to the original signal. The existence of redundancies in speech makes it possible to use encoded descriptions of the speech that can be carried in a communication channel having a bandwidth that is less than the bandwidth of the speech. This is in distinct contrast to many well-known forms of converting speech into digital signals. Most of these methods require a bandwidth that is greater than the bandwidth of the speech.
Linear predictive coding (LPC) of speech begins conceptually with a model of the human speech-producing system. The model has a source of sound that is analogous to the vocal cords. That source is coupled acoustically to a stacked array of hollow cylindrical tubes that is analogous to the cavities of the throat and mouth in a human speaker. From the model, speech is characterized by four types of quantities. The first of these is a measure of whether speech is voiced or unvoiced. A voiced signal begins with an input from the vocal cords, while an unvoiced signal is produced by the action of the rest of the vocal tract on moving air alone. This produces the differences in sound between "s" which is unvoiced and its voiced equivalent "z", for example. A second characteristic of the sound is the pitch, the fundamental frequency produced by the vocal cords in making a voiced sound. A third characteristic is the energy. Finally, the effect of the throat and mouth on either voiced or unvoiced sound is summarized by obtaining some measure of the transfer function of the vocal tract. Such a measure might be the reflection coefficients of the structure, the poles of the transfer function of the structure, the logarithmic area ratios (LAR's) of the structure, or any of several other well-known functions of such resonances. In addition, various mathematical transforms of these functions may have utility for particular purposes. The functions are interrelated so that any one set can be determined from a knowledge of any other set.
The present invention is an improved method and means of determining quantities corresponding to reflection coefficients. The reflection coefficients are coefficients with a specific all pole filter structure known as a lattice filter. The reflection coefficients discussed here are the electrical analog of acoustical reflection coefficients.
One way of obtaining the reflection coefficients that characterize a sample of a signal such as speech is to determine the characteristics of a lattice filter that will reproduce the signal when excited by an impulse. Examples of several approaches of performing such an analysis are given in an article entitled "Stable and Efficient Lattice Methods for Linear Prediction" by Makhoul in "IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-25, No. 5, October, 1977. This article points out that an analysis based on the all-pole lattice filter is stable without windowing but at a computational cost that is several times the cost of the auto-correlation and covariance methods of calculation. What Makhoul refers to as the computational costs of computer analysis are proportional to the equipment costs of realizing a circuit for comparable analysis on a semiconductor chip. When windowing of the input signal is performed prior to computation of the relection coefficients, the resulting all pole filter is guaranteed to be stable provided enough numerical precision is provided throughout the computation. Makhoul points out that with finite precision arithmetic unstable filters are indeed possible using well known autocorrelation techniques.